Candy Safety Introduces Patent-Pending LLM-Powered Detection Engine, Lowering Cloud Detection Noise to 0.04%

Candy Safety, a pacesetter in cloud runtime detection and response, at the moment introduced the launch of its groundbreaking patent-pending Giant Language Mannequin (LLM)-powered cloud detection engine. This innovation enhances Candy’s unified detection and response answer, enabling it to scale back cloud detection noise to an unprecedented 0.04%. Candy makes use of superior AI to assist safety groups navigate complicated and dynamic environments with improved precision and confidence.

Detection of Unknown Unknowns

The introduction of Candy’s patent-pending LLM expertise transforms its capability to determine beforehand undetectable threats. By evaluating cloud variables and anomalies in real-time – and adapting the findings to the nuances of the actual cloud surroundings – Candy’s cloud detection engine is able to uncovering zero-day assaults and “unknown unknowns” — threats that haven’t been launched or revealed to the world. This eliminates the necessity to predefine what constitutes irregular or malicious habits and streamlines the differentiation between uncommon exercise and precise assaults.

Quick Validation/Vindication of Findings Via Incident Labels

Candy’s patent-pending LLM-powered cloud detection engine excels at distinguishing between “bizarre” however benign anomalous exercise and real threats. Every incident is labeled as both “malicious,” “suspicious,” or “dangerous follow,” indicating whether or not the anomaly is indicative of an assault and requires additional consideration from SecOps or is an uncommon however official exercise that must be reviewed by DevOps. Safety groups can remove false positives, streamline workflows, and focus their consideration the place it issues most. The result’s unparalleled operational effectivity and diminished alert fatigue.

Actionability at Scale

To make sure most usability, the brand new functionality delivers actionable insights by:

●       Rapid mapping of “hazard zones” within the surroundings by an intuitive warmth map

●       Clear incident labeling, offering context and readability for safety analysts

●       Identification of related downside homeowners inside the group, streamlining incident response

This method improves response occasions whereas selling collaboration and accountability throughout groups.

Scaling Software Detection and Response (ADR)

In dynamic cloud environments, Candy’s patent-pending LLM-powered cloud detection engine allows scalable Software Detection and Response (ADR). It does so by cross-correlating potential assault patterns with intensive utility information to determine the ‘smoking gun’—these elusive indicators within the information which can be indicative of an assault. This functionality brings readability and precision to purposes the place the sheer quantity of knowledge would overwhelm rule-based approaches.

Elevated Certainty for Safety Groups

With the introduction of this functionality, Candy continues to ship on its mission to supply readability and management for cloud environments. By decreasing noise, enhancing detection accuracy, and empowering actionable insights, Candy will increase certainty inside safety groups, enabling them to function with confidence in even essentially the most complicated cloud landscapes.

“This new functionality is a game-changer for cloud safety,” stated Dror Kashti, CEO of Candy Safety. “By harnessing the ability of LLMs, we’re not solely decreasing detection noise to near-zero ranges but in addition offering safety groups with the instruments they should act swiftly and decisively. It is a main leap ahead in our dedication to delivering unparalleled detection and response for the cloud.”

Candy Safety is devoted to defending buyer privateness and adheres to strict privateness requirements by processing information securely and responsibly.

About Candy Safety

Candy Safety is the main supplier of Cloud Native Detection and Response options. Powered by complete runtime insights and behavioral analytics, Candy’s unified platform correlates information throughout utility, workload, and cloud infrastructure to ship best-of-breed real-time detections, in addition to vulnerability administration, id risk administration, and runtime CSPM. By analyzing baseline behaviors throughout totally different entities and using its LLM-powered detection engine, Candy reduces cloud detection noise to 0.04%, serving to organizations hit a benchmark of 2-5 min MTTR for all incidents. Privately funded, Candy is backed by Evolution Fairness Companions, Munich Re Ventures, Glilot Capital Companions, CyberArk Ventures, and an elite group of angel traders.

For extra info, customers can go to http://candy.safety.

Contact

VP of Advertising and marketing
Noa Glumcher
Candy Safety
noa@candy.safety